Robust Sparsity-Aware RLS Algorithms With Jointly-Optimized Parameters Against Impulsive Noise-Reference-Cited by-同舟云学术

Robust Sparsity-Aware RLS Algorithms With Jointly-Optimized Parameters Against Impulsive Noise

Published:2022 Issue: Volume:29 Page:1037-1041
ISSN:1070-9908
Container-title:IEEE Signal Processing Letters
language:
Short-container-title:IEEE Signal Process. Lett.

Author:

Yu Yi¹^ORCID,Lu Lu²^ORCID,Zakharov Yuriy³^ORCID,Lamare Rodrigo C. de⁴^ORCID,Chen Badong⁵^ORCID

Affiliation:

1. School of Information Engineering, Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology, Mianyang, China

2. School of Electronics and Information Engineering, Sichuan University, Chengdu, China

3. Department of Electronics, University of York, York, U.K.

4. CETUC, PUC-Rio, Rio de Janeiro, Brazil

5. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China

Funder

Nature Science Foundation of P.R. China

Southwest University of Science and Technology

Sichuan Science and Technology Program

Natural Science Foundation of Sichuan Province

U.K. EPSRC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/97/9686799/09755065.pdf?arnumber=9755065

Reference27 articles.

1. Generalized Correntropy for Robust_newline Adaptive Filtering

2. Echo cancellation in IP networks

3. Advanced television systems for terrestrial broadcasting: Some problems and some proposed solutions

4. DCD-RLS Adaptive Filters With Penalties for Sparse Identification

5. RLS Algorithm With Convex Regularization

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